In [1]:
import pandas as pd
import logging
import numpy as np
from matplotlib.pylab import plt
from statsmodels.nonparametric.smoothers_lowess import lowess
import ssl
from urllib.request import urlopen
%matplotlib inline
params = {"font.family":"Arial",
"legend.fontsize": 10,
"axes.labelsize": 12,
"axes.titlesize": 12,
"xtick.labelsize": 12,
"ytick.labelsize": 12,
"svg.fonttype": 'none'}
# plt.rcParams['svg.fonttype'] = 'none'
plt.rcParams.update(params)
logger = logging.getLogger(__name__)
logging.basicConfig(level=logging.INFO)
### COLORS ####
TUMOUR_COLOR_HP1 = "#7C3A88"
TUMOUR_COLOR_HP2 = "#8A7EC7"
NORMAL_COLOR_HP1= "#69BDC3"
NORMAL_COLOR_HP2 = "#0B8892"
### CASES WITH PROMOTER METHYLATION
BRCA1_METHYLATED_CASES_BRC_OVA = ["POG804", "POG507", "POG425"]
BRCA1_METHYLATED_CASES_NON_BRC_OVA = ["POG277", "POG1041", "POG650"]
BRCA1_LOH_CASES = ["POG804", "POG507", "POG425", "POG1041", "POG650"]
RAD51C_METHYLATED_CASES_BRC_OVA = ["POG356", "POG894", "POG846"]
RAD51C_METHYLATED_CASES_NON_BRC_OVA = ["POG785", "POG266", "POG044"]
RAD51C_LOH_CASES = ["POG356", "POG894", "POG846"]
MLH1_METHYLATED_CASES = ["POG986", "POG041"]
MLH1_LOH_CASE = ["POG986"]
In [2]:
################
#### BRCA1 #####
################
def generate_BRCA1_haplotype_plots(BRCA1_files, BRCA1_methylated_cases_BRC_OVA, BRCA1_methylated_cases_non_BRC_OVA, BRCA1_loh_cases):
tumour_types = ["BRC_OVA", "non_BRC_OVA"]
for tumour_type in tumour_types:
df_blood = pd.DataFrame()
df_tumour = pd.DataFrame()
if tumour_type == "BRC_OVA":
### Figure 5c
case_list = BRCA1_methylated_cases_BRC_OVA
else:
### Ex Fig 5b
case_list = BRCA1_methylated_cases_non_BRC_OVA
files_BRCA1 = BRCA1_files[tumour_type]
logger.info("generating BRCA1 haplotype plots for {}".format(tumour_type))
context=ssl.create_default_context()
context.set_ciphers("DEFAULT")
if len(files_BRCA1[0]) >0:
result_blood = urlopen(files_BRCA1[0], context=context)
df_blood = pd.read_csv(result_blood, sep = "\t", index_col = ["gene","start", "end"])
if len(files_BRCA1[1])>0:
result_tumour = urlopen(files_BRCA1[1], context=context)
df_tumour = pd.read_csv(result_tumour, sep = "\t", index_col = ["gene","start", "end"])
fig_BRCA1, ax_BRCA1 = plt.subplots(ncols=1, nrows=3, sharex = True, figsize = (8.5, 4.2), dpi = 600)
for patient_index, patient in enumerate(case_list):
LOH = ""
hp1_column = f"{patient}_HP1"
hp2_column = f"{patient}_HP2"
if patient in BRCA1_loh_cases:
LOH = "yes"
if not df_tumour.empty:
hp1 = df_tumour[hp1_column]
hp2 = df_tumour[hp2_column]
xpos = [value[1] for value in df_tumour.index]
### LOESS SMOOTHING TUMOUR
filtered_hp1 = lowess(hp1, xpos, is_sorted=True, frac=0.25, it=0)
x_hp1 = filtered_hp1[:, 0]
y_hp1 = filtered_hp1[:, 1]
filtered_hp2 = lowess(hp2, xpos, is_sorted=True, frac=0.25, it=0)
x_hp2 = filtered_hp2[:, 0]
y_hp2 = filtered_hp2[:, 1]
if not df_blood.empty:
if hp1_column in df_blood.columns:
hp1_blood = df_blood[hp1_column]
hp2_blood = df_blood[hp2_column]
xpos_blood = [value[1] for value in df_blood.index]
### LOESS SMOOTHING BLOOD
filtered_hp1_blood = lowess(hp1_blood, xpos_blood, is_sorted=True, frac=0.25, it=0)
x_hp1_blood = filtered_hp1_blood[:, 0]
y_hp1_blood = filtered_hp1_blood[:, 1]
filtered_hp2_blood = lowess(hp2_blood, xpos_blood, is_sorted=True, frac=0.25, it=0)
x_hp2_blood = filtered_hp2_blood[:, 0]
y_hp2_blood = filtered_hp2_blood[:, 1]
if not df_tumour.empty:
if LOH == "yes":
if patient != "POG507":
ax_BRCA1[patient_index].plot(x_hp1, y_hp1, "-", color = TUMOUR_COLOR_HP1, markerfacecolor=TUMOUR_COLOR_HP1, mec = TUMOUR_COLOR_HP1, marker="o", markersize = 4, linewidth = 0.8, label = "HP1_tumour")
ax_BRCA1[patient_index].plot(x_hp2, y_hp2, "-", color = TUMOUR_COLOR_HP2, markerfacecolor="none", mec = TUMOUR_COLOR_HP2, mew = 0.5, marker="o", markersize = 4.5, linewidth = 0.8, label = "HP2_tumour")
### flipping color because the haplotype was aribitary set for the cases
else:
ax_BRCA1[patient_index].plot(x_hp2, y_hp2, "-", color = TUMOUR_COLOR_HP1, markerfacecolor=TUMOUR_COLOR_HP1, mec = TUMOUR_COLOR_HP1, marker="o", markersize = 4, linewidth = 0.8, label = "HP1_tumour")
ax_BRCA1[patient_index].plot(x_hp1, y_hp1, "-", color = TUMOUR_COLOR_HP2, markerfacecolor="none", mec = TUMOUR_COLOR_HP2, mew = 0.5, marker="o", markersize = 4.5, linewidth = 0.8, label = "HP2_tumour")
else:
ax_BRCA1[patient_index].plot(x_hp1, y_hp1, "-", color = TUMOUR_COLOR_HP1, markerfacecolor=TUMOUR_COLOR_HP1, mec = TUMOUR_COLOR_HP1, marker="o", markersize = 4, linewidth = 0.8, label = "HP1_tumour")
ax_BRCA1[patient_index].plot(x_hp2, y_hp2, "-", color = TUMOUR_COLOR_HP2, markerfacecolor=TUMOUR_COLOR_HP2, mec = TUMOUR_COLOR_HP2, marker="o", markersize = 4, linewidth = 0.8, label = "HP2_tumour")
if not df_blood.empty:
ax_BRCA1[patient_index].plot(x_hp1_blood, y_hp1_blood, "-", color = NORMAL_COLOR_HP1, markerfacecolor=NORMAL_COLOR_HP1, mec = NORMAL_COLOR_HP1, marker="^", markersize = 4, linewidth = 0.8, label = "HP1_blood")
ax_BRCA1[patient_index].plot(x_hp2_blood, y_hp2_blood, "-", color = NORMAL_COLOR_HP2, markerfacecolor=NORMAL_COLOR_HP2, mec = NORMAL_COLOR_HP2, marker="^", markersize = 4, linewidth = 0.8, label = "HP2_blood")
ax_BRCA1[patient_index].set_ylim(-0.05, 1.1)
xlims = plt.gca().get_xlim()
xticks = ax_BRCA1[patient_index].get_xticks()
xticks = np.linspace(*xlims, 6)
ax_BRCA1[patient_index].set_xticks(xticks)
if patient_index >1:
xticks = [str(int(value)) for value in xticks]
ax_BRCA1[patient_index].set_xticklabels(xticks)
yticks = [0.0, 0.5, 1.0]
ax_BRCA1[patient_index].set_yticks(yticks)
yticks = [str(float(value)) for value in yticks]
ax_BRCA1[patient_index].set_yticklabels(yticks)
ax_BRCA1[patient_index].tick_params(axis="both")
ax_BRCA1[patient_index].set_xlabel("genomic coordinates")
ax_BRCA1[patient_index].spines["right"].set_visible(False)
ax_BRCA1[patient_index].spines["top"].set_visible(False)
else:
ax_BRCA1[patient_index].set_xticklabels("")
yticks = [0.0, 0.5, 1.0]
ax_BRCA1[patient_index].set_yticks(yticks)
yticks = [str(float(value)) for value in yticks]
ax_BRCA1[patient_index].set_yticklabels(yticks)
ax_BRCA1[patient_index].tick_params(axis="both")
if patient_index == 1:
ax_BRCA1[patient_index].set_ylabel("methyl frequencies")
ax_BRCA1[patient_index].set_ylim(-0.1, 1.05)
ax_BRCA1[patient_index].spines["right"].set_visible(False)
ax_BRCA1[patient_index].spines["top"].set_visible(False)
ax_BRCA1[patient_index].text(43126965, -0.05, patient, size = "medium", weight = "bold")
for i in range(3):
ax_BRCA1[i].set_xlim(ax_BRCA1[i].get_xlim()[::-1])
plt.legend(frameon=False, loc="lower left", bbox_to_anchor=(0.9, -0.05))
plt.tight_layout()
In [3]:
###################
##### RAD51C ######
###################
def generate_RAD51C_haplotype_plots(RAD51C_files, RAD51C_methylated_cases_BRC_OVA, RAD51C_methylated_cases_non_BRC_OVA, RAD51C_loh_cases):
tumour_types = ["BRC_OVA", "non_BRC_OVA"]
for tumour_type in tumour_types:
df_blood = pd.DataFrame()
df_tumour = pd.DataFrame()
if tumour_type == "BRC_OVA":
### Figure 5d
case_list = RAD51C_methylated_cases_BRC_OVA
else:
### Ex Fig 5c
case_list = RAD51C_methylated_cases_non_BRC_OVA
files_RAD51C = RAD51C_files[tumour_type]
context=ssl.create_default_context()
context.set_ciphers("DEFAULT")
if len(files_RAD51C[0]) >0:
result_blood = urlopen(files_RAD51C[0], context=context)
df_blood = pd.read_csv(result_blood, sep = "\t", index_col = ["gene","start", "end"])
if len(files_RAD51C[1])>0:
result_tumour = urlopen(files_RAD51C[1], context=context)
df_tumour = pd.read_csv(result_tumour, sep = "\t", index_col = ["gene","start", "end"])
logger.info("generating RAD51C haplotype plots for {}".format(tumour_type))
fig_RAD51C, ax_RAD51C = plt.subplots(ncols=1, nrows=3,sharex = True, figsize = (8.5, 4.2), dpi = 600)
for patient_index, patient in enumerate(case_list):
LOH = ""
hp1_column = f"{patient}_HP1"
hp2_column = f"{patient}_HP2"
if patient in RAD51C_loh_cases:
LOH = "yes"
if not df_tumour.empty:
hp1 = df_tumour[hp1_column]
hp2 = df_tumour[hp2_column]
xpos = [value[1] for value in df_tumour.index]
### LOESS SMOOTHING TUMOUR
filtered_hp1 = lowess(hp1, xpos, is_sorted=True, frac=0.25, it=0)
x_hp1 = filtered_hp1[:, 0]
y_hp1 = filtered_hp1[:, 1]
filtered_hp2 = lowess(hp2, xpos, is_sorted=True, frac=0.25, it=0)
x_hp2 = filtered_hp2[:, 0]
y_hp2 = filtered_hp2[:, 1]
if not df_blood.empty:
if hp1_column in df_blood.columns:
hp1_blood = df_blood[hp1_column]
hp2_blood = df_blood[hp2_column]
xpos_blood = [value[1] for value in df_blood.index]
### LOESS SMOOTHING BLOOD
filtered_hp1_blood = lowess(hp1_blood, xpos_blood, is_sorted=True, frac=0.25, it=0)
x_hp1_blood = filtered_hp1_blood[:, 0]
y_hp1_blood = filtered_hp1_blood[:, 1]
filtered_hp2_blood = lowess(hp2_blood, xpos_blood, is_sorted=True, frac=0.25, it=0)
x_hp2_blood = filtered_hp2_blood[:, 0]
y_hp2_blood = filtered_hp2_blood[:, 1]
else:
x_hp1_blood = []
y_hp1_blood = []
x_hp2_blood = []
y_hp2_blood = []
if not df_tumour.empty:
if LOH == "yes":
ax_RAD51C[patient_index].plot(x_hp1, y_hp1, "-", color = TUMOUR_COLOR_HP1, markerfacecolor=TUMOUR_COLOR_HP1, mec = TUMOUR_COLOR_HP1, marker="o", markersize = 4, linewidth = 0.8, label = "HP1_tumour")
ax_RAD51C[patient_index].plot(x_hp2, y_hp2, "-", color = TUMOUR_COLOR_HP2, markerfacecolor="none", mec = TUMOUR_COLOR_HP2, mew = 0.5, marker="o", markersize = 4.5, linewidth = 0.8, label = "HP2_tumour")
else:
ax_RAD51C[patient_index].plot(x_hp1, y_hp1, "-", color = TUMOUR_COLOR_HP1, markerfacecolor=TUMOUR_COLOR_HP1, mec = TUMOUR_COLOR_HP1, marker="o", markersize = 4, linewidth = 0.8, label = "HP1_tumour")
ax_RAD51C[patient_index].plot(x_hp2, y_hp2, "-", color = TUMOUR_COLOR_HP2, markerfacecolor=TUMOUR_COLOR_HP2, mec = TUMOUR_COLOR_HP2, marker="o", markersize = 4, linewidth = 0.8, label = "HP2_tumour")
if not df_blood.empty:
ax_RAD51C[patient_index].plot(x_hp1_blood, y_hp1_blood, "-", color = NORMAL_COLOR_HP1, markerfacecolor=NORMAL_COLOR_HP1, mec = NORMAL_COLOR_HP1, marker="^", markersize = 4, linewidth = 0.8, label = "HP1_blood")
ax_RAD51C[patient_index].plot(x_hp2_blood, y_hp2_blood, "-", color = NORMAL_COLOR_HP2, markerfacecolor=NORMAL_COLOR_HP2, mec = NORMAL_COLOR_HP2, marker="^", markersize = 4, linewidth = 0.8, label = "HP2_blood")
ax_RAD51C[patient_index].set_ylim(-0.05, 1.2)
xlims = plt.gca().get_xlim()
xticks = ax_RAD51C[patient_index].get_xticks()
xticks = np.linspace(*xlims, 6)
ax_RAD51C[patient_index].set_xticks(xticks)
if patient_index >1:
xticks = [str(int(value)) for value in xticks]
ax_RAD51C[patient_index].set_xticklabels(xticks)
yticks = [0.0, 0.5, 1.0]
ax_RAD51C[patient_index].set_yticks(yticks)
yticks = [str(float(value)) for value in yticks]
ax_RAD51C[patient_index].set_yticklabels(yticks)
ax_RAD51C[patient_index].tick_params(axis="both")
ax_RAD51C[patient_index].set_xlabel("genomic coordinates")
ax_RAD51C[patient_index].spines["right"].set_visible(False)
ax_RAD51C[patient_index].spines["top"].set_visible(False)
else:
ax_RAD51C[patient_index].set_xticklabels("")
yticks = [0.0, 0.5, 1.0]
ax_RAD51C[patient_index].set_yticks(yticks)
yticks = [str(float(value)) for value in yticks]
ax_RAD51C[patient_index].set_yticklabels(yticks)
ax_RAD51C[patient_index].tick_params(axis="both")
if patient_index == 1:
ax_RAD51C[patient_index].set_ylabel("methyl frequencies")
# ax[patient_index].legend(bbox_to_anchor=(1.04, 0.5), loc = "center left", fontsize = 9)
ax_RAD51C[patient_index].set_ylim(-0.1, 1.05)
ax_RAD51C[patient_index].spines["right"].set_visible(False)
ax_RAD51C[patient_index].spines["top"].set_visible(False)
ax_RAD51C[patient_index].text(58690993, -0.05, patient, size = "medium", weight = "bold")
plt.legend(frameon=False, loc="lower left", bbox_to_anchor=(0.9, -0.05))
plt.tight_layout()
In [4]:
##################
##### MLH1 #######
##################
def generate_MLH1_haplotype_plots(filename_MLH1_blood, filename_MLH1_tumour, MLH1_methylated_cases, MLH1_loh_case):
logger.info("generating MLH1 haplotype plots")
context=ssl.create_default_context()
context.set_ciphers("DEFAULT")
result_blood = urlopen(filename_MLH1_blood, context=context)
df_blood = pd.read_csv(result_blood, sep = "\t", index_col = ["gene","start", "end"])
result_tumour = urlopen(filename_MLH1_tumour, context=context)
df_tumour = pd.read_csv(result_tumour, sep = "\t", index_col = ["gene","start", "end"])
fig_MLH1, ax_MLH1 = plt.subplots(ncols=1, nrows=2, sharex = True, figsize = (5.2, 4), dpi = 600)
for patient_index, patient in enumerate(MLH1_methylated_cases):
LOH = ""
if patient in MLH1_loh_case:
LOH = "yes"
if not df_tumour.empty:
hp1_column = f"{patient}_HP1"
hp2_column = f"{patient}_HP2"
hp1 = df_tumour[hp1_column]
hp2 = df_tumour[hp2_column]
xpos = [value[1] for value in df_tumour.index]
filtered_hp1 = lowess(hp1, xpos, is_sorted=True, frac=0.25, it=0)
x_hp1 = filtered_hp1[:, 0]
y_hp1 = filtered_hp1[:, 1]
filtered_hp2 = lowess(hp2, xpos, is_sorted=True, frac=0.25, it=0)
x_hp2 = filtered_hp2[:, 0]
y_hp2 = filtered_hp2[:, 1]
if not df_blood.empty:
if hp1_column in df_blood.columns:
hp1_blood = df_blood[hp1_column]
hp2_blood = df_blood[hp2_column]
xpos_blood = [value[1] for value in df_blood.index]
### LOESS SMOOTHING BLOOD
filtered_hp1_blood = lowess(hp1_blood, xpos_blood, is_sorted=True, frac=0.25, it=0)
x_hp1_blood = filtered_hp1_blood[:, 0]
y_hp1_blood = filtered_hp1_blood[:, 1]
filtered_hp2_blood = lowess(hp2_blood, xpos_blood, is_sorted=True, frac=0.25, it=0)
x_hp2_blood = filtered_hp2_blood[:, 0]
y_hp2_blood = filtered_hp2_blood[:, 1]
else:
x_hp1_blood = []
y_hp1_blood = []
x_hp2_blood = []
y_hp2_blood = []
if LOH == "yes":
if not df_tumour.empty:
ax_MLH1[patient_index].plot(x_hp1, y_hp1, "-", color = TUMOUR_COLOR_HP1, markerfacecolor="none", mec = TUMOUR_COLOR_HP1, mew = 0.5, marker="o", markersize = 4.5, linewidth = 0.8, label = "HP1_tumour")
ax_MLH1[patient_index].plot(x_hp2, y_hp2, "-", color = TUMOUR_COLOR_HP2, markerfacecolor=TUMOUR_COLOR_HP2, mec = TUMOUR_COLOR_HP2, marker="o", markersize = 4, linewidth = 0.8, label = "HP2_tumour")
if not df_blood.empty:
ax_MLH1[patient_index].plot(x_hp2_blood, y_hp2_blood, "-", color = NORMAL_COLOR_HP1, markerfacecolor=NORMAL_COLOR_HP1, mec = NORMAL_COLOR_HP1, marker="^", markersize = 4, linewidth = 0.8, label = "HP1_blood")
ax_MLH1[patient_index].plot(x_hp1_blood, y_hp1_blood, "-", color = NORMAL_COLOR_HP2, markerfacecolor=NORMAL_COLOR_HP2, mec = NORMAL_COLOR_HP2, marker="^", markersize = 4, linewidth = 0.8, label = "HP2_blood")
else:
if not df_tumour.empty:
ax_MLH1[patient_index].plot(x_hp1, y_hp1, "-", color = TUMOUR_COLOR_HP1, markerfacecolor=TUMOUR_COLOR_HP1, mec = TUMOUR_COLOR_HP1, marker="o", markersize = 4, linewidth = 0.8, label = "HP1_tumour")
ax_MLH1[patient_index].plot(x_hp2, y_hp2, "-", color = TUMOUR_COLOR_HP2, markerfacecolor=TUMOUR_COLOR_HP2, mec = TUMOUR_COLOR_HP2, marker="o", markersize = 4, linewidth = 0.8, label = "HP2_tumour")
if not df_blood.empty:
ax_MLH1[patient_index].plot(x_hp2_blood, y_hp2_blood, "-", color = NORMAL_COLOR_HP1, markerfacecolor=NORMAL_COLOR_HP1, mec = NORMAL_COLOR_HP1, marker="^", markersize = 4, linewidth = 0.8, label = "HP1_blood")
ax_MLH1[patient_index].plot(x_hp1_blood, y_hp1_blood, "-", color = NORMAL_COLOR_HP2, markerfacecolor=NORMAL_COLOR_HP2, mec = NORMAL_COLOR_HP2, marker="^", markersize = 4, linewidth = 0.8, label = "HP2_blood")
ax_MLH1[patient_index].set_ylim(-0.05, 1.1)
ax_MLH1[patient_index].set_ylabel("methyl frequencies")
xlims = plt.gca().get_xlim()
xticks = ax_MLH1[patient_index].get_xticks()
xticks = np.linspace(*xlims, 5)
ax_MLH1[patient_index].set_xticks(xticks)
if patient_index >0:
xticks = [str(int(value)) for value in xticks]
ax_MLH1[patient_index].set_xticklabels(xticks)
ax_MLH1[patient_index].tick_params(axis="both")
ax_MLH1[patient_index].set_xlabel("genomic coordinates")
ax_MLH1[patient_index].spines["right"].set_visible(False)
ax_MLH1[patient_index].spines["top"].set_visible(False)
else:
ax_MLH1[patient_index].set_xticklabels("")
ax_MLH1[patient_index].tick_params(axis="both")
ax_MLH1[patient_index].spines["right"].set_visible(False)
ax_MLH1[patient_index].spines["top"].set_visible(False)
ax_MLH1[patient_index].text(36991870, -0.03, patient, size = "medium", weight = "bold")
plt.legend(frameon=False, loc="lower left", bbox_to_anchor=(0.75, -0.05))
plt.tight_layout()
In [5]:
def main():
### BRCA1 Fig 5c
filename_BRCA1_BRC_OVA_blood = r"https://www.bcgsc.ca/downloads/nanopore_pog/ts_methylation/BRCA1_blood_haplotype_methyl_freq_BRC_OVA.tsv"
filename_BRCA1_BRC_OVA_tumour = r"https://www.bcgsc.ca/downloads/nanopore_pog/ts_methylation/BRCA1_tumour_haplotype_methyl_freq_BRC_OVA.tsv"
### BRCA1 Ex Fig5b
filename_BRCA1_non_BRC_OVA_blood = r""
filename_BRCA1_non_BRC_OVA_tumour = r"https://www.bcgsc.ca/downloads/nanopore_pog/ts_methylation//BRCA1_tumour_haplotype_methyl_freq_non_BRC_OVA.tsv"
BRCA1_files = {"BRC_OVA": [filename_BRCA1_BRC_OVA_blood, filename_BRCA1_BRC_OVA_tumour],
"non_BRC_OVA":[filename_BRCA1_non_BRC_OVA_blood, filename_BRCA1_non_BRC_OVA_tumour]}
generate_BRCA1_haplotype_plots(BRCA1_files, BRCA1_METHYLATED_CASES_BRC_OVA, BRCA1_METHYLATED_CASES_NON_BRC_OVA, BRCA1_LOH_CASES)
### RAD51C Fig 5d
filename_RAD51C_BRC_OVA_blood = r"https://www.bcgsc.ca/downloads/nanopore_pog/ts_methylation/RAD51C_blood_haplotype_methyl_freq_BRC_OVA.tsv"
filename_RAD51C_BRC_OVA_tumour = r"https://www.bcgsc.ca/downloads/nanopore_pog/ts_methylation/RAD51C_tumour_haplotype_methyl_freq_BRC_OVA.tsv"
### RAD51C Ex Fig5c
filename_RAD51C_non_BRC_OVA_blood = r"https://www.bcgsc.ca/downloads/nanopore_pog/ts_methylation/RAD51C_blood_haplotype_methyl_freq_non_BRC_OVA.tsv"
filename_RAD51C_non_BRC_OVA_tumour = r"https://www.bcgsc.ca/downloads/nanopore_pog/ts_methylation/RAD51C_tumour_haplotype_methyl_freq_non_BRC_OVA.tsv"
RAD51C_files = {"BRC_OVA": [filename_RAD51C_BRC_OVA_blood, filename_RAD51C_BRC_OVA_tumour],
"non_BRC_OVA":[filename_RAD51C_non_BRC_OVA_blood, filename_RAD51C_non_BRC_OVA_tumour]}
generate_RAD51C_haplotype_plots(RAD51C_files, RAD51C_METHYLATED_CASES_BRC_OVA, RAD51C_METHYLATED_CASES_NON_BRC_OVA, RAD51C_LOH_CASES)
### MLH1 Fig 5f
filename_MLH1_blood = r"https://www.bcgsc.ca/downloads/nanopore_pog/ts_methylation/MLH1_blood_haplotype_methyl_freq.tsv"
filename_MLH1_tumour = r"https://www.bcgsc.ca/downloads/nanopore_pog/ts_methylation/MLH1_tumour_haplotype_methyl_freq.tsv"
generate_MLH1_haplotype_plots(filename_MLH1_blood, filename_MLH1_tumour, MLH1_METHYLATED_CASES, MLH1_LOH_CASE)
if __name__ == "__main__":
try:
main()
except Exception as err:
logger.critical("Failure: %s", err, exc_info=True)
INFO:__main__:generating BRCA1 haplotype plots for BRC_OVA WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found. WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found. WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found. WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found. WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found. WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found. WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found. WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found. WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found. WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found. WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found. WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found. 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INFO:__main__:generating BRCA1 haplotype plots for non_BRC_OVA WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found. WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found. WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found. WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found. WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found. WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found. WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found. WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found. WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found. WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found. WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found. WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found. WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found. WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found. WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found. WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found. WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found. WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found. WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found. WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found. WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found. INFO:__main__:generating RAD51C haplotype plots for BRC_OVA WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found. WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found. WARNING:matplotlib.font_manager:findfont: Font family 'Arial' not found. 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